Targeting moments for calibration compared with indirect inference

David Meenagh, A. Patrick Minford and Yongdeng Xu

No E2022/12, Cardiff Economics Working Papers from Cardiff University, Cardiff Business School, Economics Section

Abstract: A common practice in estimating parameters in DSGE models is to find a set that when simulated gets close to an average of certain data moments; the model's simulated performance for other moments is then compared to the data for these as an informal test of the model. We call this procedure informal Indirect Inference, III. By contrast what we call Formal Indirect Inference, FII, chooses a set of moments as the auxiliary model and computes the Wald statistic for the joint distribution of these moments according to the structural DSGE model; it tests the model according to the probability of obtaining the data moments. The FII estimator then chooses structural parameters that maximise this probability. We show in this note via Monte Carlo experiments that the FII estimator has low bias in small samples, whereas the III estimator has much higher bias. It follows that models estimated by III will typically also be rejected by formal indirect inference tests.

Keywords: Moments; Indirect Inference (search for similar items in EconPapers)
JEL-codes: C12 C32 C52 (search for similar items in EconPapers)
Pages: 6 pages
Date: 2022-07
New Economics Papers: this item is included in nep-dge, nep-ecm and nep-mac
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